Comparing LAI Estimates of Corn and Soybean from Vegetation Indices of Multi-resolution Satellite Images

Kim, Sun-Hwa; Hong, Suk Young; Sudduth, Kenneth A.; Kim, Yi-Hyun; Lee, Kyungdo

doi:10.7780/kjrs.2012.28.6.1

Cited by 11 publications

(9 citation statements)

References 25 publications

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“…Also, the expolinear model generally provided better results, as shown by higher R 2 values than those in linear or exponential models (Table 4). The reason that the NDVI-based expolinear model performed better for LAI estimation than linear or exponential models can be explained that expolinear model is closer to crop growth pattern, which combines linear and exponential curves at the same time during the growing period (Anderson et al, 2004;Lee et al, 2003;Kim et al 2012). …”

Section: Resultsmentioning

confidence: 99%

“…And the expolinear model was used for modeling crop growth and provided consistency between retrieved biophysical properties (Lee et al, 2003;Anderson et al, 2004). For the comparison of three models, coefficient of determination (R 2 ) and root mean square error (RMSE) were used (Kim et al 2012).…”

Section: Lai Estimationmentioning

confidence: 99%

“…LAI was introduced by Watson (1947) for the first time and defined as the ratio of leaf area to a given unit of land area. The ratio is functionally correlated to spectral reflectance (Kim et al 2012). LAI is an important structural parameter of vegetation canopies, which is crucial for modeling canopy interception, ET, and net photosynthesis.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Leaf Area Index of Paddy Rice from RapidEye Imagery to Assess Evapotranspiration in Korean Paddy Fields

Na¹,

Hong²,

Kim³

et al. 2013

Korean Journal of Soil Science and Fertilizer

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Section: Resultsmentioning

confidence: 99%

Section: Lai Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimating Leaf Area Index of Paddy Rice from RapidEye Imagery to Assess Evapotranspiration in Korean Paddy Fields

Na¹,

Hong²,

Kim³

et al. 2013

Korean Journal of Soil Science and Fertilizer

Self Cite

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“…In YP model using only LAI, errors in predicted yield would result from the changes in specific leaf area (SLA) in a given season and region. The SLA is the ratio of leaf area to leaf biomass [50] and is affected by environmental conditions [51], such as weather and disease [52]. As a result, the SLA value differs by season even when the same crops are cultivated at a given site [7].…”

Section: Discussionmentioning

confidence: 99%

Using MODIS Data to Predict Regional Corn Yields

Ban

Kim

Park

et al. 2016

Preprint

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A simple approach was developed to predict corn yields using the MoDerate Resolution Imaging Spectroradiometer (MODIS) data product from two geographically separate major corn crop production regions: Illinois, USA and Heilongjiang, China. The MOD09A1 data product, which are 8-day interval surface reflectance data, were obtained from day of the year (DOY) 89 to 337 to calculate the leaf area index (LAI). The sum of the LAI from early in the season to a given date in the season [end of DOY (EOD)] was well fitted to a logistic function and represented seasonal changes in leaf area duration (LAD). A simple phenology model was derived to estimate the dates of emergence and maturity using the logistic function parameters b1 and b2, which represented the rate of increase in LAI and the date of maximum LAI, respectively. The phenology model predicted emergence and maturity dates fairly well, with root mean square error (RMSE) values of 6.3 and 4.9 days for the validation dataset, respectively. Two simple linear regression models (YP and YF) were established using LAD as the variable to predict corn yield; the yield model (YP) used LAD from predicted emergence to maturity, and the yield model (YF) used LAD for a predetermined period from DOY 89 to a particular EOD. When state/province corn yields for the validation dataset were predicted at DOY 321, near completion of the corn harvest, the YP model performed much better than the YF model, with RMSE values of 0.68 t/ha and 0.66 t/ha for Illinois and Heilongjiang, respectively. The YP model showed a similar or better performance, even for the much earlier yield prediction at DOY 257, compared to that of the YF model. In conclusion, the phenology and yield models were developed based only on logistic changes in remote sensing-derived LAD, and predicted phenological dates and corn yields with considerable accuracy and precision for the two regions selected for this study. However, these models must be examined for spatial portability in more diverse agro-climatic regions.

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“…The corn yield prediction errors using only LAD result from changes in specific leaf area (SLA) in a given season and region. SLA is the ratio of leaf area to leaf biomass [60] and is affected by environmental conditions [61], such as weather and disease [52]. As a result, SLA varies by season, even when the same crops are cultivated at a given site [30].…”

Section: Discussionmentioning

confidence: 99%

Using MODIS Data to Predict Regional Corn Yields

et al. 2016

View full text Add to dashboard Cite

A simple approach was developed to predict corn yields using the MoDerate Resolution Imaging Spectroradiometer (MODIS) data product from two geographically separate major corn crop production regions: Illinois, USA and Heilongjiang, China. The MOD09A1 data, which are eight-day interval surface reflectance data, were obtained from day of the year (DOY) 89 to 337 to calculate the leaf area index (LAI). The sum of the LAI from early in the season to a given date in the season (end of DOY (EOD)) was well fitted to a logistic function and represented seasonal changes in leaf area duration (LAD). A simple phenology model was derived to estimate the dates of emergence and maturity using the LAD-logistic function parameters b 1 and b 2 , which represented the rate of increase in LAI and the date of maximum LAI, respectively. The phenology model predicted emergence and maturity dates fairly well, with root mean square error (RMSE) values of 6.3 and 4.9 days for the validation dataset, respectively. Two simple linear regression models (Y P and Y F ) were established using LAD as the variable to predict corn yield. The yield model Y P used LAD from predicted emergence to maturity, and the yield model Y F used LAD for a predetermined period from DOY 89 to a particular EOD. When state/province corn yields for the validation dataset were predicted at DOY 321, near completion of the corn harvest, the Y P model, including the predicted phenology, performed much better than the Y F model, with RMSE values of 0.68 t/ha and 0.66 t/ha for Illinois and Heilongjiang, respectively. The Y P model showed similar or better performance, even for the much earlier pre-harvest yield prediction at DOY 257. In addition, the model performance showed no difference between the two study regions with very different climates and cultivation methods, including cultivar and irrigation management. These results suggested that the approach described in this paper has potential for application to relatively wide agroclimatic regions with different cultivation methods and for extension to the other crops. However, it needs to be examined further in tropical and sub-tropical regions, which are very different from the two study regions with respect to agroclimatic constraints and agrotechnologies.

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Comparing LAI Estimates of Corn and Soybean from Vegetation Indices of Multi-resolution Satellite Images

Cited by 11 publications

References 25 publications

Estimating Leaf Area Index of Paddy Rice from RapidEye Imagery to Assess Evapotranspiration in Korean Paddy Fields

Estimating Leaf Area Index of Paddy Rice from RapidEye Imagery to Assess Evapotranspiration in Korean Paddy Fields

Using MODIS Data to Predict Regional Corn Yields

Using MODIS Data to Predict Regional Corn Yields

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